Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1010805
Missing cells2221667
Missing cells (%)12.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory131.1 MiB
Average record size in memory136.0 B

Variable types

Numeric13
Categorical3
DateTime1

Alerts

year has constant value "2016" Constant
air_temperature is highly overall correlated with dew_temperatureHigh correlation
building_id is highly overall correlated with floor_count and 1 other fieldsHigh correlation
dew_temperature is highly overall correlated with air_temperatureHigh correlation
floor_count is highly overall correlated with building_id and 2 other fieldsHigh correlation
site_id is highly overall correlated with building_id and 1 other fieldsHigh correlation
square_feet is highly overall correlated with floor_countHigh correlation
year_built has 606553 (60.0%) missing values Missing
floor_count has 835375 (82.6%) missing values Missing
cloud_coverage has 441620 (43.7%) missing values Missing
precip_depth_1_hr has 187335 (18.5%) missing values Missing
sea_level_pressure has 61585 (6.1%) missing values Missing
wind_direction has 72221 (7.1%) missing values Missing
meter_reading is highly skewed (γ1 = 104.1110474) Skewed
meter_reading has 94070 (9.3%) zeros Zeros
site_id has 53777 (5.3%) zeros Zeros
cloud_coverage has 287476 (28.4%) zeros Zeros
dew_temperature has 16966 (1.7%) zeros Zeros
precip_depth_1_hr has 725745 (71.8%) zeros Zeros
wind_direction has 118183 (11.7%) zeros Zeros
wind_speed has 118759 (11.7%) zeros Zeros

Reproduction

Analysis started2025-01-29 02:31:37.512044
Analysis finished2025-01-29 02:32:00.660037
Duration23.15 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

building_id
Real number (ℝ)

High correlation 

Distinct1449
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean798.69003
Minimum0
Maximum1448
Zeros411
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:00.693278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile98
Q1392
median895
Q31178
95-th percentile1373
Maximum1448
Range1448
Interquartile range (IQR)786

Descriptive statistics

Standard deviation426.95913
Coefficient of variation (CV)0.53457426
Kurtosis-1.2383161
Mean798.69003
Median Absolute Deviation (MAD)349
Skewness-0.31081893
Sum8.0731987 × 108
Variance182294.1
MonotonicityNot monotonic
2025-01-28T21:32:00.728187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1259 1833
 
0.2%
1241 1793
 
0.2%
1298 1791
 
0.2%
1294 1765
 
0.2%
1301 1747
 
0.2%
1296 1745
 
0.2%
1249 1744
 
0.2%
1293 1736
 
0.2%
1295 1733
 
0.2%
1232 1723
 
0.2%
Other values (1439) 993195
98.3%
ValueCountFrequency (%)
0 411
< 0.1%
1 455
< 0.1%
2 401
< 0.1%
3 442
< 0.1%
4 449
< 0.1%
5 439
< 0.1%
6 429
< 0.1%
7 834
0.1%
8 451
< 0.1%
9 791
0.1%
ValueCountFrequency (%)
1448 373
< 0.1%
1447 360
< 0.1%
1446 410
< 0.1%
1445 381
< 0.1%
1444 397
< 0.1%
1443 356
< 0.1%
1442 731
0.1%
1441 374
< 0.1%
1440 380
< 0.1%
1439 350
< 0.1%

meter
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
0
603060 
1
208925 
2
135057 
3
63763 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1010805
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 603060
59.7%
1 208925
 
20.7%
2 135057
 
13.4%
3 63763
 
6.3%

Length

2025-01-28T21:32:00.761433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T21:32:00.788983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 603060
59.7%
1 208925
 
20.7%
2 135057
 
13.4%
3 63763
 
6.3%

Most occurring characters

ValueCountFrequency (%)
0 603060
59.7%
1 208925
 
20.7%
2 135057
 
13.4%
3 63763
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1010805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 603060
59.7%
1 208925
 
20.7%
2 135057
 
13.4%
3 63763
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1010805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 603060
59.7%
1 208925
 
20.7%
2 135057
 
13.4%
3 63763
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1010805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 603060
59.7%
1 208925
 
20.7%
2 135057
 
13.4%
3 63763
 
6.3%
Distinct8784
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
Minimum2016-01-01 00:00:00
Maximum2016-12-31 23:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-01-28T21:32:00.818052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:32:00.849727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

meter_reading
Real number (ℝ)

Skewed  Zeros 

Distinct309604
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2103.913
Minimum0
Maximum20899900
Zeros94070
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:00.882979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.3
median78.7501
Q3268.054
95-th percentile1579.1
Maximum20899900
Range20899900
Interquartile range (IQR)249.754

Descriptive statistics

Standard deviation150775.02
Coefficient of variation (CV)71.664093
Kurtosis11546.098
Mean2103.913
Median Absolute Deviation (MAD)74.8799
Skewness104.11105
Sum2.1266458 × 109
Variance2.2733106 × 1010
MonotonicityNot monotonic
2025-01-28T21:32:00.913906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 94070
 
9.3%
20 1185
 
0.1%
2.9307 1173
 
0.1%
5.8614 1112
 
0.1%
36.6 1092
 
0.1%
24.4 1078
 
0.1%
8.7921 1066
 
0.1%
30 1053
 
0.1%
61 1021
 
0.1%
10 1006
 
0.1%
Other values (309594) 906949
89.7%
ValueCountFrequency (%)
0 94070
9.3%
0.0002 2
 
< 0.1%
0.0003 25
 
< 0.1%
0.0004 452
 
< 0.1%
0.0005 1
 
< 0.1%
0.0006 21
 
< 0.1%
0.0007 1
 
< 0.1%
0.0008 1
 
< 0.1%
0.0009 13
 
< 0.1%
0.0011 1
 
< 0.1%
ValueCountFrequency (%)
20899900 1
< 0.1%
20833700 1
< 0.1%
20224800 1
< 0.1%
20030300 1
< 0.1%
20005400 1
< 0.1%
19669300 1
< 0.1%
19559400 1
< 0.1%
19307100 1
< 0.1%
19211100 1
< 0.1%
19204600 1
< 0.1%

site_id
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.9857955
Minimum0
Maximum15
Zeros53777
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:00.937700image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median9
Q313
95-th percentile15
Maximum15
Range15
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.0983856
Coefficient of variation (CV)0.63843178
Kurtosis-1.5254726
Mean7.9857955
Median Absolute Deviation (MAD)5
Skewness-0.04118906
Sum8072082
Variance25.993536
MonotonicityNot monotonic
2025-01-28T21:32:01.028777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
13 135424
13.4%
9 134016
13.3%
2 126975
12.6%
14 125060
12.4%
3 118614
11.7%
15 90319
8.9%
0 53777
 
5.3%
5 39160
 
3.9%
4 37272
 
3.7%
6 33266
 
3.3%
Other values (6) 116922
11.6%
ValueCountFrequency (%)
0 53777
5.3%
1 27740
 
2.7%
2 126975
12.6%
3 118614
11.7%
4 37272
 
3.7%
5 39160
 
3.9%
6 33266
 
3.3%
7 18340
 
1.8%
8 28292
 
2.8%
9 134016
13.3%
ValueCountFrequency (%)
15 90319
8.9%
14 125060
12.4%
13 135424
13.4%
12 15744
 
1.6%
11 6081
 
0.6%
10 20725
 
2.1%
9 134016
13.3%
8 28292
 
2.8%
7 18340
 
1.8%
6 33266
 
3.3%

primary_use
Categorical

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
Education
408361 
Office
219318 
Entertainment/public assembly
113355 
Lodging/residential
107386 
Public services
83228 
Other values (11)
79157 

Length

Max length29
Median length24
Mean length12.328786
Min length5

Characters and Unicode

Total characters12461999
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLodging/residential
2nd rowTechnology/science
3rd rowEducation
4th rowEducation
5th rowEducation

Common Values

ValueCountFrequency (%)
Education 408361
40.4%
Office 219318
21.7%
Entertainment/public assembly 113355
 
11.2%
Lodging/residential 107386
 
10.6%
Public services 83228
 
8.2%
Healthcare 19909
 
2.0%
Other 12096
 
1.2%
Parking 10704
 
1.1%
Manufacturing/industrial 6161
 
0.6%
Food sales and service 5786
 
0.6%
Other values (6) 24501
 
2.4%

Length

2025-01-28T21:32:01.055495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
education 408361
33.3%
office 219318
17.9%
entertainment/public 113355
 
9.2%
assembly 113355
 
9.2%
lodging/residential 107386
 
8.8%
services 88128
 
7.2%
public 83228
 
6.8%
healthcare 19909
 
1.6%
other 12096
 
1.0%
parking 10704
 
0.9%
Other values (11) 50560
 
4.1%

Most occurring characters

ValueCountFrequency (%)
i 1403088
 
11.3%
e 1055210
 
8.5%
n 1005823
 
8.1%
c 955724
 
7.7%
t 917140
 
7.4%
a 839852
 
6.7%
d 640866
 
5.1%
u 630658
 
5.1%
o 549433
 
4.4%
s 547259
 
4.4%
Other values (26) 3916946
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12461999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1403088
 
11.3%
e 1055210
 
8.5%
n 1005823
 
8.1%
c 955724
 
7.7%
t 917140
 
7.4%
a 839852
 
6.7%
d 640866
 
5.1%
u 630658
 
5.1%
o 549433
 
4.4%
s 547259
 
4.4%
Other values (26) 3916946
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12461999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1403088
 
11.3%
e 1055210
 
8.5%
n 1005823
 
8.1%
c 955724
 
7.7%
t 917140
 
7.4%
a 839852
 
6.7%
d 640866
 
5.1%
u 630658
 
5.1%
o 549433
 
4.4%
s 547259
 
4.4%
Other values (26) 3916946
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12461999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1403088
 
11.3%
e 1055210
 
8.5%
n 1005823
 
8.1%
c 955724
 
7.7%
t 917140
 
7.4%
a 839852
 
6.7%
d 640866
 
5.1%
u 630658
 
5.1%
o 549433
 
4.4%
s 547259
 
4.4%
Other values (26) 3916946
31.4%

square_feet
Real number (ℝ)

High correlation 

Distinct1397
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean107883.35
Minimum283
Maximum875000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:01.082510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum283
5-th percentile7481
Q132766
median72709
Q3139683
95-th percentile327256
Maximum875000
Range874717
Interquartile range (IQR)106917

Descriptive statistics

Standard deviation117166.73
Coefficient of variation (CV)1.0860502
Kurtosis9.9680331
Mean107883.35
Median Absolute Deviation (MAD)48066
Skewness2.6629997
Sum1.0904903 × 1011
Variance1.3728042 × 1010
MonotonicityNot monotonic
2025-01-28T21:32:01.113804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
387638 3040
 
0.3%
150695 2613
 
0.3%
200933 2303
 
0.2%
64583 2171
 
0.2%
42755 2022
 
0.2%
71088 1833
 
0.2%
194188 1793
 
0.2%
53130 1792
 
0.2%
171084 1791
 
0.2%
65000 1784
 
0.2%
Other values (1387) 989663
97.9%
ValueCountFrequency (%)
283 470
< 0.1%
356 405
< 0.1%
366 408
< 0.1%
387 161
 
< 0.1%
481 400
< 0.1%
520 383
< 0.1%
648 388
< 0.1%
666 426
< 0.1%
755 373
< 0.1%
801 406
< 0.1%
ValueCountFrequency (%)
875000 424
 
< 0.1%
861524 1328
0.1%
850354 439
 
< 0.1%
819577 434
 
< 0.1%
809530 425
 
< 0.1%
764237 1329
0.1%
745671 1272
0.1%
731945 1320
0.1%
679614 927
0.1%
671507 1287
0.1%

year_built
Real number (ℝ)

Missing 

Distinct116
Distinct (%)< 0.1%
Missing606553
Missing (%)60.0%
Infinite0
Infinite (%)0.0%
Mean1968.2895
Minimum1900
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:01.144920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1912
Q11951
median1970
Q31993
95-th percentile2011
Maximum2017
Range117
Interquartile range (IQR)42

Descriptive statistics

Standard deviation30.196599
Coefficient of variation (CV)0.015341544
Kurtosis-0.65256433
Mean1968.2895
Median Absolute Deviation (MAD)20
Skewness-0.40611322
Sum7.9568496 × 108
Variance911.83461
MonotonicityNot monotonic
2025-01-28T21:32:01.176837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1976 26638
 
2.6%
1964 13154
 
1.3%
1966 11511
 
1.1%
2006 9755
 
1.0%
1968 9435
 
0.9%
1970 9033
 
0.9%
2007 8782
 
0.9%
1975 8672
 
0.9%
1960 8323
 
0.8%
1967 8291
 
0.8%
Other values (106) 290658
28.8%
(Missing) 606553
60.0%
ValueCountFrequency (%)
1900 2655
0.3%
1902 1091
 
0.1%
1903 1995
0.2%
1904 1162
0.1%
1905 454
 
< 0.1%
1906 1275
0.1%
1907 1701
0.2%
1908 1303
0.1%
1909 2527
0.2%
1910 2838
0.3%
ValueCountFrequency (%)
2017 450
 
< 0.1%
2016 3492
0.3%
2015 1216
 
0.1%
2014 5487
0.5%
2013 4783
0.5%
2012 3533
0.3%
2011 3342
0.3%
2010 6971
0.7%
2009 2985
0.3%
2008 2453
 
0.2%

floor_count
Real number (ℝ)

High correlation  Missing 

Distinct18
Distinct (%)< 0.1%
Missing835375
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean4.1877102
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:01.203801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile11
Maximum26
Range25
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.0157494
Coefficient of variation (CV)0.95893679
Kurtosis8.4288242
Mean4.1877102
Median Absolute Deviation (MAD)2
Skewness2.480698
Sum734650
Variance16.126243
MonotonicityNot monotonic
2025-01-28T21:32:01.225511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 46539
 
4.6%
2 34781
 
3.4%
4 17722
 
1.8%
3 16486
 
1.6%
6 14891
 
1.5%
5 12745
 
1.3%
8 8765
 
0.9%
7 6462
 
0.6%
9 4308
 
0.4%
11 3125
 
0.3%
Other values (8) 9606
 
1.0%
(Missing) 835375
82.6%
ValueCountFrequency (%)
1 46539
4.6%
2 34781
3.4%
3 16486
 
1.6%
4 17722
 
1.8%
5 12745
 
1.3%
6 14891
 
1.5%
7 6462
 
0.6%
8 8765
 
0.9%
9 4308
 
0.4%
10 1675
 
0.2%
ValueCountFrequency (%)
26 1345
 
0.1%
21 1326
 
0.1%
19 1312
 
0.1%
16 419
 
< 0.1%
14 434
 
< 0.1%
13 2217
0.2%
12 878
 
0.1%
11 3125
0.3%
10 1675
 
0.2%
9 4308
0.4%

air_temperature
Real number (ℝ)

High correlation 

Distinct614
Distinct (%)0.1%
Missing4815
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean15.984002
Minimum-28.9
Maximum47.2
Zeros7052
Zeros (%)0.7%
Negative77717
Negative (%)7.7%
Memory size7.7 MiB
2025-01-28T21:32:01.250694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-28.9
5-th percentile-2.8
Q18.5
median16.7
Q323.9
95-th percentile32.2
Maximum47.2
Range76.1
Interquartile range (IQR)15.4

Descriptive statistics

Standard deviation10.93507
Coefficient of variation (CV)0.6841259
Kurtosis-0.039492983
Mean15.984002
Median Absolute Deviation (MAD)7.7
Skewness-0.36506475
Sum16079746
Variance119.57576
MonotonicityNot monotonic
2025-01-28T21:32:01.282750image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.4 19789
 
2.0%
25 19538
 
1.9%
20 19052
 
1.9%
23.3 18823
 
1.9%
25.6 18813
 
1.9%
23.9 18741
 
1.9%
15 18740
 
1.9%
19.4 18455
 
1.8%
21.7 18380
 
1.8%
22.8 18368
 
1.8%
Other values (604) 817291
80.9%
ValueCountFrequency (%)
-28.9 15
 
< 0.1%
-28.8 1
 
< 0.1%
-28.7 4
 
< 0.1%
-28.6 6
 
< 0.1%
-28.4 2
 
< 0.1%
-28.3 17
 
< 0.1%
-28.2 8
 
< 0.1%
-28 2
 
< 0.1%
-27.8 52
< 0.1%
-27.5 1
 
< 0.1%
ValueCountFrequency (%)
47.2 61
 
< 0.1%
46.7 23
 
< 0.1%
46.1 26
 
< 0.1%
45.6 62
 
< 0.1%
45 76
 
< 0.1%
44.4 130
 
< 0.1%
43.9 288
 
< 0.1%
43.3 463
< 0.1%
42.8 525
0.1%
42.2 849
0.1%

cloud_coverage
Real number (ℝ)

Missing  Zeros 

Distinct10
Distinct (%)< 0.1%
Missing441620
Missing (%)43.7%
Infinite0
Infinite (%)0.0%
Mean1.9025712
Minimum0
Maximum9
Zeros287476
Zeros (%)28.4%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:01.308070image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile8
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.4056039
Coefficient of variation (CV)1.2643963
Kurtosis0.41654081
Mean1.9025712
Median Absolute Deviation (MAD)0
Skewness1.1600784
Sum1082915
Variance5.7869302
MonotonicityNot monotonic
2025-01-28T21:32:01.328436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 287476
28.4%
2 121174
 
12.0%
4 92282
 
9.1%
8 27643
 
2.7%
6 27399
 
2.7%
7 6203
 
0.6%
9 3689
 
0.4%
1 1278
 
0.1%
3 1102
 
0.1%
5 939
 
0.1%
(Missing) 441620
43.7%
ValueCountFrequency (%)
0 287476
28.4%
1 1278
 
0.1%
2 121174
12.0%
3 1102
 
0.1%
4 92282
 
9.1%
5 939
 
0.1%
6 27399
 
2.7%
7 6203
 
0.6%
8 27643
 
2.7%
9 3689
 
0.4%
ValueCountFrequency (%)
9 3689
 
0.4%
8 27643
 
2.7%
7 6203
 
0.6%
6 27399
 
2.7%
5 939
 
0.1%
4 92282
 
9.1%
3 1102
 
0.1%
2 121174
12.0%
1 1278
 
0.1%
0 287476
28.4%

dew_temperature
Real number (ℝ)

High correlation  Zeros 

Distinct519
Distinct (%)0.1%
Missing5006
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean7.7361903
Minimum-35
Maximum26.1
Zeros16966
Zeros (%)1.7%
Negative235486
Negative (%)23.3%
Memory size7.7 MiB
2025-01-28T21:32:01.353156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-35
5-th percentile-9.4
Q10
median8.9
Q316.1
95-th percentile22.8
Maximum26.1
Range61.1
Interquartile range (IQR)16.1

Descriptive statistics

Standard deviation10.169353
Coefficient of variation (CV)1.314517
Kurtosis-0.3340013
Mean7.7361903
Median Absolute Deviation (MAD)7.8
Skewness-0.42998524
Sum7781052.5
Variance103.41575
MonotonicityNot monotonic
2025-01-28T21:32:01.382999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 19822
 
2.0%
13.9 18410
 
1.8%
22.2 18207
 
1.8%
13.3 18097
 
1.8%
12.8 18014
 
1.8%
22.8 17943
 
1.8%
5 17721
 
1.8%
11.7 17356
 
1.7%
14.4 17052
 
1.7%
0 16966
 
1.7%
Other values (509) 826211
81.7%
ValueCountFrequency (%)
-35 2
 
< 0.1%
-34.7 1
 
< 0.1%
-34.6 4
 
< 0.1%
-34.4 5
 
< 0.1%
-34 3
 
< 0.1%
-33 7
 
< 0.1%
-32.7 24
< 0.1%
-32.6 5
 
< 0.1%
-32.3 10
< 0.1%
-32.1 4
 
< 0.1%
ValueCountFrequency (%)
26.1 25
 
< 0.1%
25.6 317
 
< 0.1%
25 1675
 
0.2%
24.4 4823
 
0.5%
24 68
 
< 0.1%
23.9 10911
1.1%
23.3 16354
1.6%
23 60
 
< 0.1%
22.9 7
 
< 0.1%
22.8 17943
1.8%

precip_depth_1_hr
Real number (ℝ)

Missing  Zeros 

Distinct128
Distinct (%)< 0.1%
Missing187335
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean0.80480528
Minimum-1
Maximum343
Zeros725745
Zeros (%)71.8%
Negative54836
Negative (%)5.4%
Memory size7.7 MiB
2025-01-28T21:32:01.413101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q10
median0
Q30
95-th percentile3
Maximum343
Range344
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7.5077527
Coefficient of variation (CV)9.3286574
Kurtosis504.16716
Mean0.80480528
Median Absolute Deviation (MAD)0
Skewness18.874581
Sum662733
Variance56.36635
MonotonicityNot monotonic
2025-01-28T21:32:01.443512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 725745
71.8%
-1 54836
 
5.4%
3 12663
 
1.3%
5 5938
 
0.6%
8 3668
 
0.4%
10 3092
 
0.3%
13 2146
 
0.2%
2 1659
 
0.2%
15 1650
 
0.2%
18 1490
 
0.1%
Other values (118) 10583
 
1.0%
(Missing) 187335
 
18.5%
ValueCountFrequency (%)
-1 54836
 
5.4%
0 725745
71.8%
2 1659
 
0.2%
3 12663
 
1.3%
4 106
 
< 0.1%
5 5938
 
0.6%
6 128
 
< 0.1%
7 304
 
< 0.1%
8 3668
 
0.4%
9 50
 
< 0.1%
ValueCountFrequency (%)
343 5
 
< 0.1%
340 12
< 0.1%
333 8
 
< 0.1%
310 13
< 0.1%
262 19
< 0.1%
257 8
 
< 0.1%
241 8
 
< 0.1%
239 20
< 0.1%
236 7
 
< 0.1%
234 8
 
< 0.1%

sea_level_pressure
Real number (ℝ)

Missing 

Distinct703
Distinct (%)0.1%
Missing61585
Missing (%)6.1%
Infinite0
Infinite (%)0.0%
Mean1016.0848
Minimum968.2
Maximum1045.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:01.473764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum968.2
5-th percentile1004.8
Q11011.6
median1016
Q31020.5
95-th percentile1027.7
Maximum1045.5
Range77.3
Interquartile range (IQR)8.9

Descriptive statistics

Standard deviation7.0703059
Coefficient of variation (CV)0.006958382
Kurtosis1.1204145
Mean1016.0848
Median Absolute Deviation (MAD)4.4
Skewness-0.10130335
Sum9.6448797 × 108
Variance49.989225
MonotonicityNot monotonic
2025-01-28T21:32:01.504786image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015.2 6337
 
0.6%
1016.2 6196
 
0.6%
1016.1 6167
 
0.6%
1015.8 6066
 
0.6%
1015.9 6028
 
0.6%
1016.4 6006
 
0.6%
1016 6004
 
0.6%
1017.7 5978
 
0.6%
1015 5855
 
0.6%
1014.5 5846
 
0.6%
Other values (693) 888737
87.9%
(Missing) 61585
 
6.1%
ValueCountFrequency (%)
968.2 2
 
< 0.1%
968.3 3
< 0.1%
968.8 5
< 0.1%
969.4 2
 
< 0.1%
969.9 1
 
< 0.1%
970.8 2
 
< 0.1%
971.6 2
 
< 0.1%
971.9 1
 
< 0.1%
972.9 1
 
< 0.1%
973.3 4
< 0.1%
ValueCountFrequency (%)
1045.5 1
 
< 0.1%
1045.4 8
< 0.1%
1045.3 3
 
< 0.1%
1045.2 8
< 0.1%
1044.9 9
< 0.1%
1044.7 3
 
< 0.1%
1044.6 4
< 0.1%
1044.5 8
< 0.1%
1044.3 8
< 0.1%
1043.9 4
< 0.1%

wind_direction
Real number (ℝ)

Missing  Zeros 

Distinct43
Distinct (%)< 0.1%
Missing72221
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean173.26702
Minimum0
Maximum360
Zeros118183
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:01.534051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q180
median180
Q3280
95-th percentile340
Maximum360
Range360
Interquartile range (IQR)200

Descriptive statistics

Standard deviation114.05568
Coefficient of variation (CV)0.65826537
Kurtosis-1.2441902
Mean173.26702
Median Absolute Deviation (MAD)100
Skewness-0.076811351
Sum1.6262565 × 108
Variance13008.698
MonotonicityNot monotonic
2025-01-28T21:32:01.563289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 118183
 
11.7%
170 32162
 
3.2%
300 31399
 
3.1%
290 29655
 
2.9%
310 28665
 
2.8%
180 28545
 
2.8%
270 27816
 
2.8%
160 27690
 
2.7%
320 27437
 
2.7%
190 26862
 
2.7%
Other values (33) 560170
55.4%
(Missing) 72221
 
7.1%
ValueCountFrequency (%)
0 118183
11.7%
10 16852
 
1.7%
20 16100
 
1.6%
30 15516
 
1.5%
40 14461
 
1.4%
50 15043
 
1.5%
60 17730
 
1.8%
70 20714
 
2.0%
80 21606
 
2.1%
84 3
 
< 0.1%
ValueCountFrequency (%)
360 21139
2.1%
350 24158
2.4%
340 23899
2.4%
330 25023
2.5%
320 27437
2.7%
310 28665
2.8%
300 31399
3.1%
290 29655
2.9%
280 26396
2.6%
270 27816
2.8%

wind_speed
Real number (ℝ)

Zeros 

Distinct58
Distinct (%)< 0.1%
Missing7157
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean3.3820312
Minimum0
Maximum19
Zeros118759
Zeros (%)11.7%
Negative0
Negative (%)0.0%
Memory size7.7 MiB
2025-01-28T21:32:01.594020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.1
median3.1
Q34.6
95-th percentile7.7
Maximum19
Range19
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.2659987
Coefficient of variation (CV)0.67001118
Kurtosis1.1418642
Mean3.3820312
Median Absolute Deviation (MAD)1.5
Skewness0.81369787
Sum3394368.8
Variance5.13475
MonotonicityNot monotonic
2025-01-28T21:32:01.683171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 118759
11.7%
2.1 116156
11.5%
2.6 109783
10.9%
1.5 104188
10.3%
3.1 99240
9.8%
3.6 88430
8.7%
4.1 72200
7.1%
4.6 60710
6.0%
5.1 48062
 
4.8%
5.7 39090
 
3.9%
Other values (48) 147030
14.5%
ValueCountFrequency (%)
0 118759
11.7%
0.5 1950
 
0.2%
1 4257
 
0.4%
1.3 21
 
< 0.1%
1.5 104188
10.3%
1.6 5
 
< 0.1%
2 1163
 
0.1%
2.1 116156
11.5%
2.2 46
 
< 0.1%
2.6 109783
10.9%
ValueCountFrequency (%)
19 6
 
< 0.1%
18.5 4
 
< 0.1%
18 5
 
< 0.1%
17 21
 
< 0.1%
16.5 14
 
< 0.1%
16 66
< 0.1%
15.4 80
< 0.1%
15 27
 
< 0.1%
14.9 87
< 0.1%
14.4 131
< 0.1%

year
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.7 MiB
2016
1010805 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4043220
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 1010805
100.0%

Length

2025-01-28T21:32:01.738504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-28T21:32:01.754341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 1010805
100.0%

Most occurring characters

ValueCountFrequency (%)
2 1010805
25.0%
0 1010805
25.0%
1 1010805
25.0%
6 1010805
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4043220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1010805
25.0%
0 1010805
25.0%
1 1010805
25.0%
6 1010805
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4043220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1010805
25.0%
0 1010805
25.0%
1 1010805
25.0%
6 1010805
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4043220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1010805
25.0%
0 1010805
25.0%
1 1010805
25.0%
6 1010805
25.0%

Interactions

2025-01-28T21:31:57.604834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:47.840549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.774051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:49.533868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:50.536581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:51.491437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.058244image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.654212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:53.517012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:54.208148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.073044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.807728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:56.651960image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:57.680899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:47.918219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.829632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:49.609983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:50.625094image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:51.537317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.094976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.732777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:53.569824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:54.282714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.130684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.874060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:56.720534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:57.753395image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.015577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.882614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:49.686307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:50.696767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:51.585676image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.134103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.805683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:53.627158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:54.353679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.212311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.943975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:56.787856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:57.830317image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.087878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.941159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:49.760460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:50.773238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:51.635066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.169976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.883889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:53.703134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:54.423031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.273384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:56.013403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:56.865555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:57.881541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.140790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:48.984524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:49.809040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:50.829312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:51.679233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.206598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:52.935098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:53.742727image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:54.468088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:55.312674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-01-28T21:31:56.061539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2025-01-28T21:32:01.773087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
air_temperaturebuilding_idcloud_coveragedew_temperaturefloor_countmetermeter_readingprecip_depth_1_hrprimary_usesea_level_pressuresite_idsquare_feetwind_directionwind_speedyear_built
air_temperature1.000-0.2970.1210.730-0.2210.0800.0190.0500.046-0.320-0.299-0.011-0.097-0.0570.184
building_id-0.2971.000-0.239-0.117-0.5790.3170.136-0.0410.2240.0740.9940.1410.0580.005-0.315
cloud_coverage0.121-0.2391.0000.168-0.1230.130-0.050-0.0350.076-0.147-0.241-0.0880.0550.2370.082
dew_temperature0.730-0.1170.1681.000-0.1860.0800.0250.0660.043-0.192-0.118-0.045-0.165-0.1240.131
floor_count-0.221-0.579-0.123-0.1861.0000.2190.4460.0230.204-0.047-0.5960.6300.0910.0060.006
meter0.0800.3170.1300.0800.2191.0000.0180.0080.1220.0580.2780.1170.0660.0570.118
meter_reading0.0190.136-0.0500.0250.4460.0181.0000.0020.003-0.0130.1350.499-0.018-0.0420.130
precip_depth_1_hr0.050-0.041-0.0350.0660.0230.0080.0021.0000.005-0.021-0.0410.006-0.035-0.0360.027
primary_use0.0460.2240.0760.0430.2040.1220.0030.0051.0000.0260.2050.1510.0340.0270.172
sea_level_pressure-0.3200.074-0.147-0.192-0.0470.058-0.013-0.0210.0261.0000.074-0.047-0.095-0.176-0.004
site_id-0.2990.994-0.241-0.118-0.5960.2780.135-0.0410.2050.0741.0000.1400.0590.005-0.321
square_feet-0.0110.141-0.088-0.0450.6300.1170.4990.0060.151-0.0470.1401.000-0.017-0.0500.123
wind_direction-0.0970.0580.055-0.1650.0910.066-0.018-0.0350.034-0.0950.059-0.0171.0000.420-0.060
wind_speed-0.0570.0050.237-0.1240.0060.057-0.042-0.0360.027-0.1760.005-0.0500.4201.000-0.037
year_built0.184-0.3150.0820.1310.0060.1180.1300.0270.172-0.004-0.3210.123-0.060-0.0371.000

Missing values

2025-01-28T21:31:58.499674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-28T21:31:59.037288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-01-28T21:32:00.363970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

building_idmetertimestampmeter_readingsite_idprimary_usesquare_feetyear_builtfloor_countair_temperaturecloud_coveragedew_temperatureprecip_depth_1_hrsea_level_pressurewind_directionwind_speedyear
021512016-06-11 15:00:00103.32502Lodging/residential364061940.0NaN28.92.016.10.01011.080.03.62016
1132922016-01-16 15:00:00254.253015Technology/science601001912.0NaN2.8NaN2.2NaN998.8230.02.12016
210802016-01-18 23:00:00194.00001Education815801913.05.00.00.0-3.6NaN1015.550.01.52016
327102016-04-21 02:00:0045.91002Education71421NaNNaN32.24.0-8.30.01007.9300.03.12016
4110702016-07-29 23:00:00576.657013Education184098NaNNaN25.62.010.60.01016.510.04.12016
5122622016-05-14 18:00:000.424614Education27995NaNNaN22.84.09.40.01004.4230.06.22016
6115702016-04-29 08:00:00141.125013Education37107NaNNaN1.72.01.10.01021.620.02.12016
722812016-01-03 14:00:0096.43202Lodging/residential491081951.0NaN11.72.00.00.01018.190.03.62016
890312016-09-09 11:00:002022.08009Education275793NaNNaN25.00.023.30.01012.5NaN2.12016
998912016-05-31 11:00:00340.29009Education168696NaNNaN23.9NaN22.20.01011.4NaN1.52016
building_idmetertimestampmeter_readingsite_idprimary_usesquare_feetyear_builtfloor_countair_temperaturecloud_coveragedew_temperatureprecip_depth_1_hrsea_level_pressurewind_directionwind_speedyear
1010795111502016-01-12 07:00:0032.29513Services42028NaNNaN-17.22.0-21.1-1.01014.2320.08.22016
101079619202016-09-02 20:00:00722.9902Education1516371983.0NaN37.84.015.00.01007.4NaN3.12016
101079796302016-11-08 14:00:0022.0009Lodging/residential44784NaNNaN17.80.017.20.01020.90.00.02016
101079812902016-02-21 23:00:00127.3001Lodging/residential1029571968.07.011.7NaN10.1NaN1008.0230.010.32016
1010799108002016-03-09 22:00:00412.71313Education207115NaNNaN5.0NaN1.70.01014.6210.03.62016
101080046802016-05-05 13:00:0069.7203Office40000NaNNaN10.6NaN9.4-1.01004.930.04.62016
101080186302016-07-10 12:00:00113.6258Public services19823NaN1.027.82.023.90.01018.60.00.02016
1010802114102016-04-11 07:00:0022.00013Parking20986NaNNaN1.1NaN-7.80.01011.2300.04.62016
101080365802016-05-02 03:00:003.2005Entertainment/public assembly310751976.02.011.00.010.0NaNNaN220.02.12016
101080455202016-03-07 02:00:009.4503Public services201101936.0NaN5.6NaN0.60.01026.4170.01.52016